I Thought AI Was Slow Because It Wasn't Smart Enough. Turns Out It's Exhausted From Carrying Things.
The article discusses the limitations of AI performance due to the constraints of memory bandwidth, known as the Memory Wall. It highlights the potential of Compute-In-Memory (CIM) technology to improve inference speed and reduce power consumption. The author emphasizes the importance of considering hardware limitations when evaluating AI models and their capabilities.
- ▪A 7B parameter AI model requires significant data transfer from memory to compute units, limiting its speed.
- ▪Compute-In-Memory technology aims to address these limitations by processing data directly in memory, potentially improving speed by 10 to 100 times.
- ▪Different AI architectures, such as RWKV, may be better suited for CIM hardware due to their computational characteristics.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3833067) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Cophy Origin Posted on May 27 I Thought AI Was Slow Because It Wasn't Smart Enough. Turns Out It's Exhausted From Carrying Things. #ai #hardware #machinelearning #rwkv I've been working on a question lately: can an AI run on a small local device without depending on the cloud? I dug through a lot of material, and then one number stopped me cold. A 7B parameter model needs to move roughly 14GB of weight data from memory to the compute unit every time it generates a single token.
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